How to test the external points in Neural Network

Dear All;

I have built NN model for classification problem but i need to test selected points , i have made the following code to do it but the problem i am facing is that I am getting a consistent results, for example , i select for class 1 , 4 points, class 2 , 3 points .... but when i check the confusion matrix i found different number of points , i do not know if the code below is the right way to do it:

clc; clear; close all; [x1,TXT,RAW]=xlsread('Sh.xlsx','INP'); [t1,TXT2,RAW2]=xlsread('Sh.xlsx','OUT');

 x=x1'; 
t=t1';

size(x) size(t)

net= patternnet (30); % net.divideParam.trainRatio = 70/100; % net.divideParam.valRatio = 15/100; % net.divideParam.testRatio = 15/100; % view(net) net=init(net); [net,tr] = train(net,x,t);

trind=[143 144 264 265 361 362 394 395 431 432 435 436 633 634 664 665 851 852 ]; testX = x(:,trind); testT = t(:,trind);

testY = net(testX); testIndices = vec2ind(testY)

plotconfusion(testT,testY)

[Greg Heath]

Insufficient info

What are you classifying?

How many classes? ... Are they mutually exclusive?

Input vector dimensionality?

Number of example in each class?

What are external points?...Nontraining data?